<< std::endl; // 推理 context->enqueueV2(buffers, stream, nullptr); // 显存到内存 float prob[10]; cudaMemcpyAsync(prob, buffers[1], 1 * nOutputSize * sizeof(float), cudaMemcpyDeviceToHost, stream); // 同步结束,释放资源 cudaStreamSynchronize(stream); cudaStreamDestroy(stream); 解析输出...
后处理部分也是把输出转为cv::Mat然后用CTC的方式解码,这部分参考paddleOCRv3之一: rec识别部分用 openVINO(C++)部署 下面是三个比较关键的步骤,初始化模型、推理、释放模型和其他资源,有了这些就已经很明显了,仔细看看下面的步骤结合tensorRT的文档就没有什么问题了。 在这里实际使用的时候可以用设计模式中的template ...
The inference has been upgraded utilizingenqueueV3insteadenqueueV2. To maintain legacy support for TensorRT 8, adedicated branchhas been created.Outdated We've added a new optionval_trt.sh --generate-graphwhich enablesGraph Renderingfunctionality. This feature facilitates the creation of graphical repre...
Inference execution is kicked off using the context’sexecuteV2orenqueueV3methods. After the execution, we copy the results to a host buffer and release all device memory allocations. context->setTensorAddress(input_name,input_mem);context->setTensorAddress(output_name,output_mem);boolstatus=contex...
context) { return false; } bool status = context->enqueueV2(bindings.data(), stream, nullptr); if (!status) { std::cout << "ERROR: TensorRT inference failed" << std::endl; return false; } 4. 后处理:从输出 bindings 取出数据,根据输出格式处理数据 用cv::Mat 接收输出的前景 fgr 和...
TensorRT execution is typically asynchronous, so enqueue the kernels on a CUDA stream: context->enqueueV2(buffers, stream, nullptr); It is common to enqueue asynchronous memcpy() before and after the kernels to move data from the GPU if it is not already there. The final argument to enqueue...
The inference has been upgraded utilizingenqueueV3insteadenqueueV2. To maintain legacy support for TensorRT 8, adedicated branchhas been created.Outdated We've added a new optionval_trt.sh --generate-graphwhich enablesGraph Renderingfunctionality. This feature facilitates the creation of graphical repre...
For example, in a call to ExecutionContext::enqueueV3(), the execution context was created from an engine, which was created from a runtime, so TensorRT will use the logger associated with that runtime. The primary method of error handling is the ErrorRecorder interface. You can ...
‣ Call enqueueV3() on the execution context to run inference. NVIDIA TensorRT 8.5.10 Developer Guide SWE-SWDOCTRT-005-DEVG | 6 TensorRT's Capabilities The Engine interface represents an optimized model. You can query an engine for information about the input and outp...
imgBackbone 部署属于常规操作,没有特别需要注意的问题,毕竟Sparse4Dv3 imgBackbone 的组成为:Resnet50+FPN,大家再熟悉不过了。这里,我随机测试三个样本,下图直接贴上验证结果: 图三十一:PyTorch vs. ONNX Runtime 推理一致性验证结果 图三十二:PyTorch vs. TensorRT API 推理一致性验证结果 图三十三:imgBackbone po...